Abstract
BackgroundPersonal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method.MethodsThis study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios.ResultsThe results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual’s home and all the other daily activity locations.ConclusionsThis new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.
Highlights
Personal privacy is a significant concern in the era of big data
Various number of potential activity locations around home In Scenario 1 (S1), we manipulate the number of potential activity locations around the home from 1 to 50 to see how daily activity locations (DAL) k-anonymity varies with changing numbers of potential activity locations around home ( Nph ) and how the results are different from spatial k-anonymity
Various time spent at home In Scenario 2 (S2), we manipulate the time the person spends at home (T h ) from 6 to 24 h to see how DAL k-anonymity varies with the changing time spent at home and how the results are different from spatial k-anonymity
Summary
In the field of health geography, per‐ sonal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. In the field of health geography, personal health data are collected with geographic location information including residential addresses and daily activity locations to evaluate individual environmental exposures [1,2,3,4,5,6,7,8]. While high-accuracy geospatial data facilitate the improvements in health geography studies, personal location information can be linked to other digital data sources and may help the identification of individuals [9,10,11]. For health geography researchers who handle personal geospatial data, assuring individual geographic privacy and confidentiality—geoprivacy—is an important topic [12]. Geoprivacy is an emerging topic that attracts much attention from both researchers and the general public [13, 14]
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